Evidence map›Paper›PMID 35471631›Full record

ReviewInfection2023

Coronavirus: a comparative analysis of detection technologies in the wake of emerging variants.

Shagun Sharma, Surabhi Shrivastava, Shankar B Kausley, Beena Rai, Aniruddha B Pandit

Open access · bronzeAbstract readReview
In one paragraph

Review in Infection, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
3.1field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 31 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Shagun SharmaDepartment of Zoology, University of Rajasthan, JLN Marg, Jaipur, 302004, India.
Surabhi ShrivastavaPhysical Sciences Research Area, TCS Research, Tata Research Development and Design Centre, Tata Consultancy Services Limited, Pune, 411013, India.
Shankar B KausleyPhysical Sciences Research Area, TCS Research, Tata Research Development and Design Centre, Tata Consultancy Services Limited, Pune, 411013, India. shankar.kausley@tcs.com.
Beena RaiPhysical Sciences Research Area, TCS Research, Tata Research Development and Design Centre, Tata Consultancy Services Limited, Pune, 411013, India.
Aniruddha B PanditDepartment of Chemical Engineering, Institute of Chemical Technology, Matunga, Mumbai, 400019, India.
Tata Consultancy Services (India) · INInstitute of Chemical Technology · INUniversity of Rajasthan · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An outbreak of the coronavirus disease caused by a novel pathogen created havoc and continues to affect the entire world. As the pandemic progressed, the scientific community was faced by the limitations of existing diagnostic methods. In this review, we have compared the existing diagnostic techniques such as reverse transcription polymerase chain reaction (RT-PCR), antigen and antibody detection, computed tomography scan, etc. and techniques in the research phase like microarray, artificial intelligence, and detection using novel materials; on the prospect of sample preparation, detection procedure (qualitative/quantitative), detection time, screening efficiency, cost-effectiveness, and ability to detect different variants. A detailed comparison of different techniques showed that RT-PCR is still the most widely used and accepted coronavirus detection method despite certain limitations (single gene targeting- in context to mutations). New methods with similar efficiency that could overcome the limitations of RT-PCR may increase the speed, simplicity, and affordability of diagnosis. In addition to existing devices, we have also discussed diagnostic devices in the research phase showing high potential for clinical use. Our approach would be of enormous benefit in selecting a diagnostic device under a given scenario, which would ultimately help in controlling the current pandemic caused by the coronavirus, which is still far from over with new variants emerging.

Indexed as

COVID-19Artificial IntelligenceHumansSARS-CoV-2COVID-19Diagnostic devicesInfectious diseasePoint-of-careSARS-CoV-2Variants

Identifiers

PMID35471631
PMCPMC9038995
OpenAlexW4224438240

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.